class_docstr: Whitley objective function.
  
  This class defines the Whitley [1]_ global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{Whitley}}(x) = \sum_{i=1}^n \sum_{j=1}^n
                              \left[\frac{(100(x_i^2-x_j)^2
                              + (1-x_j)^2)^2}{4000} - \cos(100(x_i^2-x_j)^2
                              + (1-x_j)^2)+1 \right]
  
  
  Here, :math:`n` represents the number of dimensions and
  :math:`x_i \in [-10.24, 10.24]` for :math:`i = 1, ..., n`.
  
  *Global optimum*: :math:`f(x) = 0` for :math:`x_i = 1` for
  :math:`i = 1, ..., n`
  
  .. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
  
  TODO Jamil#167 has '+ 1' inside the cos term, when it should be outside it.
code: def fun(self, x, *args):
      self.nfev += 1
  
      XI = x
      XJ = atleast_2d(x).T
  
      temp = 100.0 * ((XI**2.0) - XJ) + (1.0 - XJ) ** 2.0
      inner = (temp**2.0 / 4000.0) - cos(temp) + 1.0
      return sum(sum(inner, axis=0))
  
imports: from numpy import atleast_2d, arange, sum, cos, exp, pi
  from .go_benchmark import Benchmark
label_desc: Use image node_id 2 for calling the Whitley obj's underlying member method code with example usage: obj.fun(x) and returns: sum